arXiv subjects

Minghui Li

Publications and source records attributed to Minghui Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction

Accurate prediction of Drug-Target Affinity (DTA) is crucial for reducing experimental costs and accelerating early screening in computational drug discovery. While sequence-based deep learning methods avoid reliance on costly 3D structures, they still overlook simultaneous modeling of global sequence semantic features and local topological structural features within drugs and proteins, and represent drugs as flat sequences without atomic-level, substructural-level, and molecular-level multi-scale features. We propose HiF-DTA, a hierarchical network that adopts a dual-pathway strategy to extract both global sequence semantic and local topological features from drug and protein sequences, and models drugs multi-scale to learn atomic, substructural, and molecular representations fused via a multi-scale bilinear attention module. Experiments on Davis, KIBA, and Metz datasets show HiF-DTA outperforms state-of-the-art baselines, with ablations confirming the importance of global-local extraction and multi-scale fusion.

cs.LG↗

arXiv2025

SegTrans: Transferable Adversarial Examples for Segmentation Models

Segmentation models exhibit significant vulnerability to adversarial examples in white-box settings, but existing adversarial attack methods often show poor transferability across different segmentation models. While some researchers have explored transfer-based adversarial attack (i.e., transfer attack) methods for segmentation models, the complex contextual dependencies within these models and the feature distribution gaps between surrogate and target models result in unsatisfactory transfer success rates. To address these issues, we propose SegTrans, a novel transfer attack framework that divides the input sample into multiple local regions and remaps their semantic information to generate diverse enhanced samples. These enhanced samples replace the original ones for perturbation optimization, thereby improving the transferability of adversarial examples across different segmentation models. Unlike existing methods, SegTrans only retains local semantic information from the original input, rather than using global semantic information to optimize perturbations. Extensive experiments on two benchmark datasets, PASCAL VOC and Cityscapes, four different segmentation models, and three backbone networks show that SegTrans significantly improves adversarial transfer success rates without introducing additional computational overhead. Compared to the current state-of-the-art methods, SegTrans achieves an average increase of 8.55% in transfer attack success rate and improves computational efficiency by more than 100%.

cs.CV↗

arXiv2025

Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces

Deepfakes, leveraging advanced AIGC (Artificial Intelligence-Generated Content) techniques, create hyper-realistic synthetic images and videos of human faces, posing a significant threat to the authenticity of social media. While this real-world threat is increasingly prevalent, existing academic evaluations and benchmarks for detecting deepfake forgery often fall short to achieve effective application for their lack of specificity, limited deepfake diversity, restricted manipulation techniques.To address these limitations, we introduce RedFace (Real-world-oriented Deepfake Face), a specialized facial deepfake dataset, comprising over 60,000 forged images and 1,000 manipulated videos derived from authentic facial features, to bridge the gap between academic evaluations and real-world necessity. Unlike prior benchmarks, which typically rely on academic methods to generate deepfakes, RedFace utilizes 9 commercial online platforms to integrate the latest deepfake technologies found "in the wild", effectively simulating real-world black-box scenarios.Moreover, RedFace's deepfakes are synthesized using bespoke algorithms, allowing it to capture diverse and evolving methods used by real-world deepfake creators. Extensive experimental results on RedFace (including cross-domain, intra-domain, and real-world social network dissemination simulations) verify the limited practicality of existing deepfake detection schemes against real-world applications. We further perform a detailed analysis of the RedFace dataset, elucidating the reason of its impact on detection performance compared to conventional datasets. Our dataset is available at: https://github.com/kikyou-220/RedFace.

cs.CV↗

arXiv2025

DarkHash: A Data-Free Backdoor Attack Against Deep Hashing

Benefiting from its superior feature learning capabilities and efficiency, deep hashing has achieved remarkable success in large-scale image retrieval. Recent studies have demonstrated the vulnerability of deep hashing models to backdoor attacks. Although these studies have shown promising attack results, they rely on access to the training dataset to implant the backdoor. In the real world, obtaining such data (e.g., identity information) is often prohibited due to privacy protection and intellectual property concerns. Embedding backdoors into deep hashing models without access to the training data, while maintaining retrieval accuracy for the original task, presents a novel and challenging problem. In this paper, we propose DarkHash, the first data-free backdoor attack against deep hashing. Specifically, we design a novel shadow backdoor attack framework with dual-semantic guidance. It embeds backdoor functionality and maintains original retrieval accuracy by fine-tuning only specific layers of the victim model using a surrogate dataset. We consider leveraging the relationship between individual samples and their neighbors to enhance backdoor attacks during training. By designing a topological alignment loss, we optimize both individual and neighboring poisoned samples toward the target sample, further enhancing the attack capability. Experimental results on four image datasets, five model architectures, and two hashing methods demonstrate the high effectiveness of DarkHash, outperforming existing state-of-the-art backdoor attack methods. Defense experiments show that DarkHash can withstand existing mainstream backdoor defense methods.

cs.CV↗

arXiv2025

ADVEDM:Fine-grained Adversarial Attack against VLM-based Embodied Agents

Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks in embodied agents, such as autonomous driving and robotic manipulation. Recent research has increasingly explored adversarial attacks on VLMs to reveal their vulnerabilities. However, these attacks either rely on overly strong assumptions, requiring full knowledge of the victim VLM, which is impractical for attacking VLM-based agents, or exhibit limited effectiveness. The latter stems from disrupting most semantic information in the image, which leads to a misalignment between the perception and the task context defined by system prompts. This inconsistency interrupts the VLM's reasoning process, resulting in invalid outputs that fail to affect interactions in the physical world. To this end, we propose a fine-grained adversarial attack framework, ADVEDM, which modifies the VLM's perception of only a few key objects while preserving the semantics of the remaining regions. This attack effectively reduces conflicts with the task context, making VLMs output valid but incorrect decisions and affecting the actions of agents, thus posing a more substantial safety threat in the physical world. We design two variants of based on this framework, ADVEDM-R and ADVEDM-A, which respectively remove the semantics of a specific object from the image and add the semantics of a new object into the image. The experimental results in both general scenarios and EDM tasks demonstrate fine-grained control and excellent attack performance.

cs.CV↗

arXiv2025

Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling

Direct Prompt Injection (DPI) attacks pose a critical security threat to Large Language Models (LLMs) due to their low barrier of execution and high potential damage. To address the impracticality of existing white-box/gray-box methods and the poor transferability of black-box methods, we propose an activations-guided prompt injection attack framework. We first construct an Energy-based Model (EBM) using activations from a surrogate model to evaluate the quality of adversarial prompts. Guided by the trained EBM, we employ the token-level Markov Chain Monte Carlo (MCMC) sampling to adaptively optimize adversarial prompts, thereby enabling gradient-free black-box attacks. Experimental results demonstrate our superior cross-model transferability, achieving 49.6% attack success rate (ASR) across five mainstream LLMs and 34.6% improvement over human-crafted prompts, and maintaining 36.6% ASR on unseen task scenarios. Interpretability analysis reveals a correlation between activations and attack effectiveness, highlighting the critical role of semantic patterns in transferable vulnerability exploitation.

cs.AI↗

arXiv2025

An inverse moving point source problem in electromagnetics

This paper is concerned with an inverse moving point source problem in electromagnetics. The aim is to reconstruct the moving orbit from the tangential components of magnetic fields taken at a finite number of observation points. The distance function between each observation point and the moving point source is computed by solving a nonlinear ordinary differential equation with an initial value. This ODE system only involves the measurement data from the tangential trace of the magnetic field at observation points. As a consequence, the dynamical measurement data recorded at four non-coplanar points are sufficient to reconstruct the orbit function. A Lipschitz stability is established for the inverse problem, and numerical experiments are reported to demonstrate the effectiveness of the proposed method. Numerical examples have shown that the reconstructed error depends linearly on the noise level and that the wave speed is a critical factor affecting the relative error.

math.NA↗

arXiv2025

The FAST Globular Cluster Pulsar Survey (GC FANS)

By January 2025, 60 pulsars were discovered by the Five-hundred-meter Aperture Spherical radio Telescope globular cluster (GC) pulsar survey (GC FANS), with spin periods spanning 1.98 ms to 3960.72 ms. Of these, 55 are millisecond pulsars (MSPs; $P<30$ ms), while 34 are binaries with orbital periods spanning 0.12 days to 466.47 days. This paper describes GC FANS, a deep, thorough search for pulsars in 41 GCs in the FAST sky ($-14^\circ < δ< 65^\circ$) and describes new discoveries in 14 of them. We present updated timing solutions for M92A, NGC 6712A, M71A, and M71E, all of which are ``spider'' pulsars with short orbital periods. We present new timing solutions for M71B, C, and D. With orbital periods of $\sim$466 and 378 days, M71B and M71C are the widest known GC binaries; these systems resemble the normal wide MSP-He WD systems in the Galactic disk. With a spin period of 101 ms, M71D is in an eccentric ($e\sim$0.63) orbit with an 11-day period and a massive companion; the system has a total mass of $2.63 \pm 0.08 \, M_{\odot}$. These features and its large characteristic age suggest it is a double neutron star system (DNS) formed via massive binary evolution early in the cluster's history, akin to Galactic disk DNSs--unlike other candidate GC DNSs, which typically form dynamically. A comparative analysis of GC pulsar populations within FAST's sky reveals that most clusters (10 of 14) resemble the Galactic disk MSP population, likely due to lower stellar densities.

astro-ph.HE↗

arXiv2025

New highly efficient and accurate numerical scheme for the Cahn-Hilliard-Brinkman system

In this paper, based on a generalized scalar auxiliary variable approach with relaxation (R-GSAV), we construct a class of high-order backward differentiation formula (BDF) schemes with variable time steps for the Cahn-Hilliard-Brinkman(CHB) system. In theory, it is strictly proved that the designed schemes are unconditionally energy-stable. With the delicate treatment of adaptive strategies, we propose several adaptive time-step algorithms to enhance the robustness of the schemes. More importantly, a novel hybrid-order adaptive time steps algorithm performs outstanding for the coupled system. The hybrid-order algorithm inherits the advantages of some traditional high-order BDF adaptive strategies. A comprehensive comparison with some adaptive time-step algorithms is given, and the advantages of the new adaptive time-step algorithms are emphasized. Finally, the effectiveness and accuracy of the new methods are validated through a series of numerical experiments.

math.NA↗

arXiv2025

Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning

Modern robot navigation systems encounter difficulties in diverse and complex indoor environments. Traditional approaches rely on multiple modules with small models or rule-based systems and thus lack adaptability to new environments. To address this, we developed Astra, a comprehensive dual-model architecture, Astra-Global and Astra-Local, for mobile robot navigation. Astra-Global, a multimodal LLM, processes vision and language inputs to perform self and goal localization using a hybrid topological-semantic graph as the global map, and outperforms traditional visual place recognition methods. Astra-Local, a multitask network, handles local path planning and odometry estimation. Its 4D spatial-temporal encoder, trained through self-supervised learning, generates robust 4D features for downstream tasks. The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot. Deployed on real in-house mobile robots, Astra achieves high end-to-end mission success rate across diverse indoor environments.

cs.RO↗

arXiv2025

Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning

Accurate prediction of molecular metabolic stability (MS) is critical for drug research and development but remains challenging due to the complex interplay of molecular interactions. Despite recent advances in graph neural networks (GNNs) for MS prediction, current approaches face two critical limitations: (1) incomplete molecular modeling due to atom-centric message-passing mechanisms that disregard bond-level topological features, and (2) prediction frameworks that lack reliable uncertainty quantification. To address these challenges, we propose TrustworthyMS, a novel contrastive learning framework designed for uncertainty-aware metabolic stability prediction. First, a molecular graph topology remapping mechanism synchronizes atom-bond interactions through edge-induced feature propagation, capturing both localized electronic effects and global conformational constraints. Second, contrastive topology-bond alignment enforces consistency between molecular topology views and bond patterns via feature alignment, enhancing representation robustness. Third, uncertainty modeling through Beta-Binomial uncertainty quantification enables simultaneous prediction and confidence calibration under epistemic uncertainty. Through extensive experiments, our results demonstrate that TrustworthyMS outperforms current state-of-the-art methods in terms of predictive performance.

cs.LG↗

arXiv2025

Spa-VLM: Stealthy Poisoning Attacks on RAG-based VLM

With the rapid development of the Vision-Language Model (VLM), significant progress has been made in Visual Question Answering (VQA) tasks. However, existing VLM often generate inaccurate answers due to a lack of up-to-date knowledge. To address this issue, recent research has introduced Retrieval-Augmented Generation (RAG) techniques, commonly used in Large Language Models (LLM), into VLM, incorporating external multi-modal knowledge to enhance the accuracy and practicality of VLM systems. Nevertheless, the RAG in LLM may be susceptible to data poisoning attacks. RAG-based VLM may also face the threat of this attack. This paper first reveals the vulnerabilities of the RAG-based large model under poisoning attack, showing that existing single-modal RAG poisoning attacks have a 100\% failure rate in multi-modal RAG scenarios. To address this gap, we propose Spa-VLM (Stealthy Poisoning Attack on RAG-based VLM), a new paradigm for poisoning attacks on large models. We carefully craft malicious multi-modal knowledge entries, including adversarial images and misleading text, which are then injected into the RAG's knowledge base. When users access the VLM service, the system may generate misleading outputs. We evaluate Spa-VLM on two Wikipedia datasets and across two different RAGs. Results demonstrate that our method achieves highly stealthy poisoning, with the attack success rate exceeding 0.8 after injecting just 5 malicious entries into knowledge bases with 100K and 2M entries, outperforming state-of-the-art poisoning attacks designed for RAG-based LLMs. Additionally, we evaluated several defense mechanisms, all of which ultimately proved ineffective against Spa-VLM, underscoring the effectiveness and robustness of our attack.

cs.CR↗

arXiv2025

2.7-octave supercontinuum generation spanning from ultraviolet to near-infrared in thin-film lithium niobate waveguides

Supercontinuum generation (SCG) with spectral coverage across the full visible and ultraviolet (UV) ranges is crucial for optical clocks, quantum computing and sensing. However, achieving such SCG in nanophotonic platforms is challenging due to the difficulties in spectrum broadening. Here, Such ultrabroad-bandwidth SCG was demonstrated in thin-film lithium niobate (TFLN) nanophotonic waveguides by dispersion management, without periodic poling for spectral broadening. Anomalous-dispersion waveguides were designed in the telecom band, simultaneously enabling dispersive wave emergence, modal-matched second harmonic generation, and third harmonic generation for spectrum broadening. Moreover, MgO was intentionally doped to mitigate the photorefractive effect of lithium niobate, which frequently results in un-sustained spectrum broadening and in turn limits the accessible SCG coverage. By leveraging photolithography assisted chemo-mechanical etching, low-loss MgO doped TFLN nanophotonic waveguides were fabricated. As a result, thanks to the utilization of the strong second-order and third-order nonlinear processes, gap-free 2.7-octave SCG spanning from 330 nm to 2250 nm was observed by pumping the waveguide with a 1550-nm femtosecond pulsed laser with 0.687 nJ, agreeing well with numerical simulation. This spectral coverage represents the state of the art in TFLN platforms without fine microdomains, and even close to the record in sophisticated chirped periodically poled TFLN waveguides.

physics.optics↗

arXiv2025

Searching for pulsars in Globular Clusters with the Fast Fold Algorithm and a new pulsar discovered in M13

We employed the Fast Folding Algorithm (FFA) on L-Band Globular Cluster (GC) observations taken with Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for new pulsars, especially those with a long rotational period. We conducted a search across 16 GCs that collectively host 93 known pulsars, as well as 14 GCs that do not contain any known pulsars. The majority of these known pulsars were successfully re-detected in our survey. The few non-detections could be attributed to the high accelerations of these pulsars. Additionally, we have discovered a new binary millisecond pulsar, namely M13I (or PSR J1641+3627I) in GC M13 (or NGC 6205), and obtained its phase-coherent timing solution using observations spanning 6 years. M13I has a spin period of 6.37 ms, and an orbital period of 18.23 days. The eccentricity of the binary orbit is 0.064, with a companion mass range of approximately 0.45 to 1.37 M$_{\odot}$. The orbital properties of M13I are remarkably different from those of the other known pulsars in M13, indicating that this pulsar has undergone a different evolutionary path compared to the rest.

astro-ph.HE↗

arXiv2025

Low-loss thin-film periodically poled lithium niobate waveguides fabricated by femtosecond laser photolithography

Periodically poled lithium niobate on insulator (PPLNOI) ridge waveguides are critical photonic components for both classical and quantum information processing. However, dry etching of PPLNOI waveguides often generates rough sidewalls and variations in the etching rates of oppositely poled lithium niobate ferroelectric domains, leading a relatively high propagation losses (0.25 - 1 dB/cm), which significantly limits net conversion efficiency and hinders scalable photonic integration. In this work, a low-loss PPLNOI ridge waveguide with a length of 7 mm was fabricated using ultra-smooth sidewalls through photolithography-assisted chemo-mechanical etching (PLACE) followed by high-voltage pulse poling with low cost. The average surface roughness was measured at just 0.27 nm, resulting in record-low propagation loss of 0.106 dB/cm in PPLNOI waveguides. Highly efficient second-harmonic generation was demonstrated with a normalized efficiency of 1643%/(W*cm^2) without temperature tuning, corresponding to a conversion efficiency of 805%/W, which is closed to the best conversion efficiency (i.e., 814%/W) reported in nanophotonic PPLNOI waveguide fabricated by expensive electron-beam lithography followed by dry etching. The absolute conversion efficiency reached 15.8% at a pump level of 21.6 mW. And the normalized efficiency can be even improved to 1742%/(W*cm^2) at optimal temperature of 59°C.

physics.optics↗

arXiv2025

FAST Observation and Results for Core Collapse Globular Cluster M15 and NGC 6517

Radio astronomy is part of radio science that developed rapidly in recent decades. In the research of radio astronomy, pulsars have always been an enduring popular research target. To find and observe more pulsars, large radio telescopes have been built all over the world. In this paper, we present our studies on pulsars in M15 and NGC 6517 with FAST, including monitoring pulsars in M15 and new pulsar discoveries in NGC 6517. All the previously known pulsars in M15 were detected without no new discoveries. Among them, M15C was still detectable by FAST, while it is assumed to fade out due to precession [1]. In NGC 6517, new pulsars were continues to be discovered and all of them are tend to be isolated pulsars. Currently, the number of pulsars in NGC 6517 is 17, much more than the predicted before [2].

astro-ph.HE↗

arXiv2025

Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization

Deep neural networks (DNNs) are susceptible to universal adversarial perturbations (UAPs). These perturbations are meticulously designed to fool the target model universally across all sample classes. Unlike instance-specific adversarial examples (AEs), generating UAPs is more complex because they must be generalized across a wide range of data samples and models. Our research reveals that existing universal attack methods, which optimize UAPs using DNNs with static model parameter snapshots, do not fully leverage the potential of DNNs to generate more effective UAPs. Rather than optimizing UAPs against static DNN models with a fixed training set, we suggest using dynamic model-data pairs to generate UAPs. In particular, we introduce a dynamic maximin optimization strategy, aiming to optimize the UAP across a variety of optimal model-data pairs. We term this approach DM-UAP. DM-UAP utilizes an iterative max-min-min optimization framework that refines the model-data pairs, coupled with a curriculum UAP learning algorithm to examine the combined space of model parameters and data thoroughly. Comprehensive experiments on the ImageNet dataset demonstrate that the proposed DM-UAP markedly enhances both cross-sample universality and cross-model transferability of UAPs. Using only 500 samples for UAP generation, DM-UAP outperforms the state-of-the-art approach with an average increase in fooling ratio of 12.108%.

cs.LG↗

arXiv2025

Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets

Transfer learning from pre-trained encoders has become essential in modern machine learning, enabling efficient model adaptation across diverse tasks. However, this combination of pre-training and downstream adaptation creates an expanded attack surface, exposing models to sophisticated backdoor embeddings at both the encoder and dataset levels--an area often overlooked in prior research. Additionally, the limited computational resources typically available to users of pre-trained encoders constrain the effectiveness of generic backdoor defenses compared to end-to-end training from scratch. In this work, we investigate how to mitigate potential backdoor risks in resource-constrained transfer learning scenarios. Specifically, we conduct an exhaustive analysis of existing defense strategies, revealing that many follow a reactive workflow based on assumptions that do not scale to unknown threats, novel attack types, or different training paradigms. In response, we introduce a proactive mindset focused on identifying clean elements and propose the Trusted Core (T-Core) Bootstrapping framework, which emphasizes the importance of pinpointing trustworthy data and neurons to enhance model security. Our empirical evaluations demonstrate the effectiveness and superiority of T-Core, specifically assessing 5 encoder poisoning attacks, 7 dataset poisoning attacks, and 14 baseline defenses across five benchmark datasets, addressing four scenarios of 3 potential backdoor threats.

cs.LG↗